Unit 2 Walkthrough: Hashtag Common Core
ECI 589 Social Network Analysis and Education
Dr. Shaun Kellogg
June 29, 2021
1. PREPARE
Our Unit 2 Walkthrough: Hashtag Common Core is inspired by the work of Jonathan Supovitz, Alan Daly, Miguel del Fresno and Christian Kolouch who examined the intense debate surrounding the Common Core State Standards education reform as it played out on Twitter. As noted on their expansive and interactive website for the #COMMONCORE Project, the Common Core was a major education policy initiatives of the early 21st century that sought to strengthen education systems across the United States through a set of specific and challenging education standards. Although these standards once enjoyed bipartisan support, we’ll see in this walkthrough how these standards have become a political punching bag, particularly around the math standards introduced.
In addition to investigating tweets around the these controversial state standards, our Unit 2 Walkthrough will cover the following topics pertaining to each data-intensive workflow process:
Prepare: Prior to analysis, we’ll take a look at the context from which our data came, formulate some research questions, and get introduced the {rtweet} R package for using the Twitter API.
Wrangle: Wrangling data entails the work of manipulating, cleaning, transforming, and merging data. In section 2 we will learn about the {tidygraph} package for creating network objects.
Explore: In section 3, we use the {tidygraph} package and the companion {ggraph} package to calculate a range of centrality measures and learn how to illustrate some of these stats through network visualization.
Model: We wrap up our analysis in Section 4 by introducing community detection and sentiment analysis algorithms for identifying groups and gauging sentiment about the common core.
Communicate: We briefly reflect on our walkthrough in preparation for our independent analysis next week.
1a. Review the Research
Recall from Social Network Analysis and Education: Theory, Methods & Applications that Carolyn (2013) cited the following four features used by Freeman (2004) to define the social network perspective:
Social network analysis is motivated by a relational intuition based on ties connecting social actors.
It is firmly grounded in systematic empirical data.
It makes use of graphic imagery to represent actors and their relations with one another.
It relies on mathematical and/or computational models to succinctly represent the complexity of social life.
The #COMMONCORE Project that we’ll briefly examine next is an exemplary illustration of these four defining features of the social network perspective.
The #commoncore Project
Supovitz, J., Daly, A.J., del Fresno, M., & Kolouch, C. (2017). #commoncore Project. Retrieved from http://www.hashtagcommoncore.com.
Prologue
As noted by Supovitz et al. (2017), the Common Core State Standards have been a “persistent flashpoint in the debate over the direction of American education.” The #commoncore Project explores the Common Core debate on Twitter using a combination of social network analyses and psychological investigations which help to reveal both the underlying social structure of the conversation and the motivations of the participants.
The central question guiding this investigation was:
How are social media-enabled social networks changing the discourse in American politics that produces and sustains social policy?
Data Sources & Analyses
The methods page of the #COMMONCORE Project provides a detailed discussion of the methods used to arrive at the conclusions in #commoncore: How social media is changing the politics of education. Provided below is a summary of how authors retrieved the Twitter data and the analyses for each of the five acts in the website. I highly encourage you to take a look at this section if you’d like to learn more about their approach and in particular if you’re unfamiliar with how users can interact and communicate on Twitter.
Data Collection
To collect data on keywords related to the Common Core, the project used a customized data collection tool developed by two of our co-authors, Miguel del Fresno and Alan J. Daly, called Social Runner LabTM. Similar to an approach we’ll use later in this walkthrough, the authors downloaded data in real time directly from Twitter’s Application Programming Interface (API) based on tweets using specified keywords, keyphrases, or hashtags and then restricted their analysis to the following terms: commoncore, ccss and stopcommoncore. They also captured Twitter profile names, or user names, as well as the tweets, retweets, and mentions posted. Data included messages that are public on twitter, but not private messages between individuals, nor from accounts which users have made private or direct messages.
Analyses
In order to address their research question, the authors applied social network analysis techniques in addition to qualitative and automated text mining approaches. For social network analyses, each node is an individual Twitter user (person, group, institution, etc.) and the connection between each node is the tweet, retweet, or mention/reply. After retrieving data from the Twitter API, the authors created a file that could be analyzed in Gephi, an open-source software program which depicts the relations as networks and provides metrics for describing features of the network.
In addition to data visualization and network descriptives, the authors ran a community detection algorithm to identify and represent structural sub-communities, or factions (a “faction” in this sense is a group with more ties within than across group even those group boundaries are somewhat porous).
For a nice summary of the data used for the analysis, as well as the samples of actors and tweets, the keywords, and the methods that were utilized, see Table 1. Data and Method for Each Act in the Methods section of the #commoncore website.
Key Findings
In the #commoncore Project, analyses of almost 1 million tweets sent by about 190,000 distinct actors over a period of 32 months revealed the following:
- In Act 1, The Giant Network, the authors identified five major sub-communities, or factions, in the Twitter debate surrounding the Common Core, including: (1) supporters of the Common Core, (2) opponents of the standards from inside education, and (3) opponents from outside of education.
- In Act 2, Central Actors, they noted that most of these participants were casual contributors – almost 95% of them made fewer than 10 tweets in any given six-month period. They also distinguished between two types of influence on Twitter: Transmitters who tweeted a lot, regardless of the extent of their followership; and Transceivers, those who gained their influence by being frequently retweeted and mentioned.
- In Act 3, Key Events, the authors identified issues driving the major spikes in the conversation, like when Secretary of Education Duncan spoke about white suburban moms’ opposition to the Common Core, or the debate over the authorization of the Every Student Succeeds Act in November 2015. They also offended evidence of manufactured controversies spurred by sensationalizing minor issues and outright fake news stories.
- In Act 4, Lexical Tendencies, the authors examined the linguistic tendencies of the three major factions and found that Common Core supporters used the highest number of conviction words, tended to use more achievement-oriented language, and used more words associated with a formal and analytic thinking style. By contrast, opponents of the Common Core from within education tended to use more words associated with sadness, and used more narrative thinking style language. Opponents of the Common Core from outside of education made the highest use of words associated with peer affiliation, used the largest number of angry words, and exhibited the lowest level of conviction in their word choices.
- In Act 5, The Tweet Machine, examined five frames that opponents of the Common Core used to appeal to values of particular subgroups including the government frame, business frame, war frame, experiment frame, and propaganda frame. By combining these constituencies, the authors illustrated how the Common Core developed a strong transpartisan coalition of opposition.
👉 Your Turn ⤵
For our Unit 2 Walkthrough, we’ll apply some of the same techniques used by this study including some quick attempts at applying community detection algorithms and automated text analysis using a few new packages in R.
Some of the techniques and approaches used in this study, however, you are likely already familiar with high. For example, take a quick look at the Explore the Networks section from Act 2: Central Actors and the Transmitters, Transceivers and Transcenders identified by their analysis.
In the space below, type a brief response to the following questions:
What network measures introduced in Unit 1 do think were used to identify these three types of central actors?
Now check your response by looking at the methods page of the #COMMONCORE Project.
Were you correct?
1b. Identify a Question(s)
Recall from above that the central question guiding the #COMMONCORE Project was:
How are social media-enabled social networks changing the discourse in American politics that produces and sustains social policy?
For Unit 2, we are going to focus our questions on something a bit less ambitious but inspired by this work:
- Who are the transmitters, transceivers, and transcenders in our Common Core Twitter network?
- What subgroups, or factions, exist in our network?
- Which actors in our network tend to be more opposed to the Common Core?
To address the latter question, we’ll introduce a common text mining technique in the Model section for gauging sentiment of social media posts.
👉 Your Turn ⤵
Based on what you know about networks and the context so far, what other research questions might ask we ask in this context that a social network perspective might be able to answer?
In the space below, type a brief response to the following questions:
- YOUR RESPONSE HERE
1c. Set Up Project
As highlighted in Chapter 6 of Data Science in Education Using R (DSIEUR), one of the first steps of every workflow should be to set up your “Project” within RStudio. Recall that…
A Project is the home for all of the files, images, reports, and code that are used in any given project
Since we are working in RStudio Cloud, a Project has already been set up for you as indicated by the eci-589.Rproj file in your main directory in the Files pane.
Load Libraries
In Unit 1, we also learned about packages, or libraries, which are shareable collections of R code that can contain functions, data, and/or documentation and extend the functionality of R. You can always check to see which packages have already been installed and loaded into RStudio Cloud by looking at the the Files, Plots, & Packages Pane in the lower right hand corner.
Let’s go ahead and load the packages from Unit 1 since we’ll be using them again:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.6
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:purrr':
##
## compose, simplify## The following object is masked from 'package:tidyr':
##
## crossing## The following object is masked from 'package:tibble':
##
## as_data_frame## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionlibrary(tidytext)rtweet 📦
Similar to approach used by Supovitz et al. (2017) for downloading data in directly from Twitter’s Application Programming Interface (API), we will be using {rtweet} package to search for and download tweets based on specified keywords.
The rtweet package provides users a range of functions designed to extract data from Twitter’s REST and streaming APIs and has three main goals:
Formulate and send requests to Twitter’s REST and stream APIs.
Retrieve and iterate over returned data.
Wrangling data into tidy structures.
Let’s load the {rtweet} package that we’ll be using in just a bit to accomplish all three of the goals listed above:
library(rtweet)##
## Attaching package: 'rtweet'## The following object is masked from 'package:purrr':
##
## flatten1c. AccessTwitter API
Before you can begin downloading data using the Twitter API, you’ll first need to create a Twitter App in your developer account. You are not required to set up developer account for this course, but if you are still interested in creating one, these instructions outline the process and you can set one up in about 10 minutes.
If you are not interested in setting an account of your own, I can provide you with temporary keys and tokens to download data from Twitter and have also provided data for this walkthrough in the data folder.
What is an API?
For those new to the concept of an API, our very own Raleigh, NC-based Red Hat describes an API as a means for one product or service communicate with other products and services:
APIs are sometimes thought of as contracts, with documentation that represents an agreement between parties: If party 1 sends a remote request structured a particular way, this is how party 2’s software will respond.
As Duke University’s Chris Bail notes, “APIs have become one of the most important ways to access and transfer data online— and increasingly APIs can even analyze your data as well.” Compared to screen-scraping data from web pages, which explore in Module 2 later this year, APIs are considerably easier to work with than the HTML or XML data, ensure that data is collected legally, and with R packages like rtweet, are logistically easier to work with, as we’ll see at the workshop this summer.
For a more in-depth explanation of APIs, how they work, and a preview of some of the rtweet functions we’ll be using this summer, we highly recommend reading through Chris Bail’s Application Programming Interfaces in R tutorial.
Authorize RStudio
In order to authorize R to use your Twitter App to retrieve data, you’ll need to create a personal Twitter token by completing the following steps:
- Navigate to developer.twitter.com/en/apps and select your Twitter app
- Click the tab labeled
Keys and tokensto retrieve your keys. - Locate the
Consumer API keys(aka “API Secret”).
- Scroll down to
Access token & access token secretand clickCreate
- Locate the following code into the R Script file named
twitter-auth.Rlocated in files pane, replace the four fake keys with your own, and pass them along tocreate_token()function.
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"
## authenticate via web browser
token <- create_token(
app = app_name,
consumer_key = api_key,
consumer_secret = api_secret_key,
access_token = access_token,
access_secret = access_token_secret)Note: these keys are named secret for a reason. We store these up in a separate R script file rather than in a R Markdown file that you will eventually share.
2. WRANGLE
In general, data wrangling involves some combination of cleaning, reshaping, transforming, and merging data (Wickham and Grolemund 2016). The importance of data wrangling is difficult to overstate, as it involves the initial steps of going from the raw data to a dataset that can be explored and modeled (Krumm, Means, and Bienkowski 2018).
For our data wrangling this week, we’re keeping it simple since working with network data is a bit of a departure from our working with rectangular data frames. Our primary goals for Unit 1 are learning how to:
Import Tweets. In this section, we introduce the
rtweetpackage and some key functions to search for tweets or users of interest.Create a Network Object. Before performing network analyses, we’ll need to convert our data frames into special data format for working with relational data.
Simplify Network. Finally, we’ll learn about a handy
simplify()function in the {igraph} package for collapsing multiple ties between actors and removing “self-loops.”
2a. Import Tweets
The Import Tweets section introduces the following functions from the rtweet package for reading Twitter data into R:
search_tweets()Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms.search_tweets2()Returns data from multiple search queries.get_timelines()Returns up to 3,200 tweets of one or more specified Twitter users.
Search Tweets
Since one of our goals for this walkthrough is a very crude replication of the study by Supovitz et al. (2017), let’s begin by introducing the search_tweets() function to try reading into R 5,000 tweets containing the commoncore hashtag and store as a new data frame ngss_all_tweets.
Type or copy the following code into your R script or console and run:
ccss_tweets <- search_tweets(q = "#commoncore", n=5000)You likely saw in the output or console a download message like the one below that stopped well short of 100%. That is pretty typical and just indicates the API returned fewer tweets than requested.
Note also that the first argument q = that the search_tweets() function expects is the search term included in quotation marks and that n = specifies the maximum number of tweets
👉 Your Turn ⤵
Use the code chunk below to wiew your new ccss_tweets data frame using one of the various view methods for looking at your data in R:
#YOUR CODE HEREHow many tweets did our query using the Twitter API actually return? How many variables?
- YOUR RESPONSE HERE
Why do you think our query pulled in far less than 5,000 tweets requested? Hint: Type
?search_tweetsin your console to see the support documentation for this function.- YOUR RESPONSE HERE
Does our query also include retweets? How do you know?
- YOUR RESPONSE HERE
Remove Retweets
While the authors explicitly mentioned including retweets when constructing their network, search_tweets() function has an argument that includes retweets by default, but can be changed to exclude retweets if desired.
Even though we will want to include retweets to construct our final network, let’s test out the include_rts = argument and set it to FALSE to remove any retweets:
ccss_tweets_1 <- search_tweets("#commoncore",
n=500,
include_rts = FALSE)Take a look at your environment pane and see how many tweets were excluded by adding this argument.
Using the OR Operator
If you recall from the Prepare section, the authors accessed tweets and user information from using multiple search terms, including: commoncore, ccss and stopcommoncore.
Let’s modify our query using the OR operator to also include “ccss” and “stopcommoncore” so it will return tweets containing either any of these three terms. And since we’ve included additional search terms let’s also increase the number of requested tweets to 5,000.
ccss_tweets_2 <- search_tweets(q = "#commoncore OR #stopcommoncore OR #ccss",
n=5000)Wow, those additional search terms dramatically increased the number of tweets downloaded!
👉 Your Turn ⤵
Try including both search terms but excluding the OR operator to answer the following question:
Does excluding the
ORoperator return more tweets, the same number of tweets, or fewer tweets? Why?- YOUR RESPONSE HERE
What other useful arguments does the
search_tweet()function contain? Try adding one and see what happens.- YOUR RESPONSE HERE
Hint: Use the ?search_tweets help function to learn more about the q argument and other arguments for composing search queries.
Filter for English Tweets
If you haven’t already done so, take a look at the tweets in our ccss_tweets_2 data frame. You probably noticed that many of these tweets were in Spanish, as indicated by the “es” in the lang column of our data frame.
Let’s take a quick count of the number of tweets in each language of our data frame by sending our ccss_tweets_2 data frame to the count() function using the %>% operator and specifying the lang as the column whose values we want to count and sorting from high to low:
ccss_tweets_2 %>%
count(lang, sort = TRUE)## # A tibble: 8 x 2
## lang n
## <chr> <int>
## 1 es 68
## 2 en 44
## 3 fr 4
## 4 ja 2
## 5 pt 2
## 6 und 2
## 7 ca 1
## 8 it 1Surprisingly, the majority of these tweets are in Spanish. For the purpose of both limiting the size of our network for this analysis and also because we are interested in doing some quick sentiment analysis towards the end of our analysis, let’s limit our tweets to just those in the English language.
To download only English tweets, we will need include the lang = argument and set it to “en”:
ccss_tweets_3 <- search_tweets(q = "#commoncore OR #stopcommoncore OR #ccss",
n=5000,
lang = "en")Save Tweets to File
Finally, let’s save our tweet files to use in later exercises since tweets have a tendency to change every minute.
write_csv(ccss_tweets_3, "data/ccss-tweets.csv")Uh oh! We received an error because data stored in the hashtags column is not stored as typical character, number, logical, or date data type, but rather it is stored as a “list.”
Use the glimpse() function to see which other columns store data as lists:
glimpse(ccss_tweets_3)## Rows: 44
## Columns: 90
## $ user_id <chr> "4574806453", "1704888684", "12459668095991070…
## $ status_id <chr> "1409717268246798354", "1409572572749078530", …
## $ created_at <dttm> 2021-06-29 03:36:02, 2021-06-28 18:01:04, 202…
## $ screen_name <chr> "__WORD_PLAY__", "gbheducation", "DistanceLrnB…
## $ text <chr> "Innocent Hebrew\nhttps://t.co/g9T0ABi9L5\nInn…
## $ source <chr> "Twitter for Android", "Sprout Social", "Dista…
## $ display_text_width <dbl> 282, 276, 140, 279, 271, 140, 140, 280, 128, 1…
## $ reply_to_status_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "14090…
## $ reply_to_user_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "12159…
## $ reply_to_screen_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "sfchr…
## $ is_quote <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ is_retweet <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, …
## $ favorite_count <int> 0, 0, 0, 1, 1, 0, 0, 6, 0, 0, 1, 0, 0, 0, 0, 0…
## $ retweet_count <int> 0, 0, 1, 1, 0, 3, 3, 3, 0, 0, 0, 0, 0, 13, 2, …
## $ quote_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ reply_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hashtags <list> "CommonCore", <"NonVisual", "ELL", "SciChat",…
## $ symbols <list> NA, NA, NA, NA, NA, NA, NA, <"LRN", "COUR">, …
## $ urls_url <list> <"biblescan.com/searchhebrew.p…", "biblescan.…
## $ urls_t.co <list> <"https://t.co/g9T0ABi9L5", "https://t.co/F1X…
## $ urls_expanded_url <list> <"https://biblescan.com/searchhebrew.php?q=in…
## $ media_url <list> NA, NA, NA, NA, NA, NA, NA, NA, "http://pbs.t…
## $ media_t.co <list> NA, NA, NA, NA, NA, NA, NA, NA, "https://t.co…
## $ media_expanded_url <list> NA, NA, NA, NA, NA, NA, NA, NA, "https://twit…
## $ media_type <list> NA, NA, NA, NA, NA, NA, NA, NA, "photo", NA, …
## $ ext_media_url <list> NA, NA, NA, NA, NA, NA, NA, NA, "http://pbs.t…
## $ ext_media_t.co <list> NA, NA, NA, NA, NA, NA, NA, NA, "https://t.co…
## $ ext_media_expanded_url <list> NA, NA, NA, NA, NA, NA, NA, NA, "https://twit…
## $ ext_media_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ mentions_user_id <list> NA, NA, "905765240603684865", NA, NA, <"11841…
## $ mentions_screen_name <list> NA, NA, "k12movieguides", NA, NA, <"JoeWEquil…
## $ lang <chr> "en", "en", "en", "en", "en", "en", "en", "en"…
## $ quoted_status_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_text <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_created_at <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ quoted_source <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_favorite_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_retweet_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_user_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_screen_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_followers_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_friends_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_statuses_count <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_location <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_description <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ quoted_verified <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ retweet_status_id <chr> NA, NA, "1409446331261669380", NA, NA, "140933…
## $ retweet_text <chr> NA, NA, "#Luca Movie Guide | Worksheet | Quest…
## $ retweet_created_at <dttm> NA, NA, 2021-06-28 09:39:26, NA, NA, 2021-06-…
## $ retweet_source <chr> NA, NA, "Twitter Web App", NA, NA, "Twitter fo…
## $ retweet_favorite_count <int> NA, NA, 1, NA, NA, 6, 6, NA, NA, NA, NA, NA, N…
## $ retweet_retweet_count <int> NA, NA, 1, NA, NA, 3, 3, NA, NA, NA, NA, NA, N…
## $ retweet_user_id <chr> NA, NA, "905765240603684865", NA, NA, "1184184…
## $ retweet_screen_name <chr> NA, NA, "k12movieguides", NA, NA, "JoeWEquil",…
## $ retweet_name <chr> NA, NA, "K12movieguides", NA, NA, "Joe WEquil"…
## $ retweet_followers_count <int> NA, NA, 42, NA, NA, 273, 273, NA, NA, NA, NA, …
## $ retweet_friends_count <int> NA, NA, 106, NA, NA, 641, 641, NA, NA, NA, NA,…
## $ retweet_statuses_count <int> NA, NA, 634, NA, NA, 484, 484, NA, NA, NA, NA,…
## $ retweet_location <chr> NA, NA, "Global", NA, NA, "Virginia, USA", "Vi…
## $ retweet_description <chr> NA, NA, "On the quest for movie educational ma…
## $ retweet_verified <lgl> NA, NA, FALSE, NA, NA, FALSE, FALSE, NA, NA, N…
## $ place_url <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ place_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ place_full_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ place_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ country <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ country_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ geo_coords <list> <NA, NA>, <NA, NA>, <NA, NA>, <NA, NA>, <NA, …
## $ coords_coords <list> <NA, NA>, <NA, NA>, <NA, NA>, <NA, NA>, <NA, …
## $ bbox_coords <list> <NA, NA, NA, NA, NA, NA, NA, NA>, <NA, NA, NA…
## $ status_url <chr> "https://twitter.com/__WORD_PLAY__/status/1409…
## $ name <chr> "princessofthaking", "GBH Education", "#Distan…
## $ location <chr> "[[415]] ", "Boston, MA", "San Diego", "Global…
## $ description <chr> "#Libertarian\n#DoublePisces\n#VirgoMoon\n#Enn…
## $ url <chr> "https://t.co/aBqiu928MF", "http://t.co/EDX7Gt…
## $ protected <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ followers_count <int> 421, 5343, 1399, 42, 42, 104, 1545, 273, 9, 88…
## $ friends_count <int> 664, 2228, 0, 106, 106, 681, 4, 641, 36, 9368,…
## $ listed_count <int> 0, 175, 7, 0, 0, 0, 27, 3, 1, 604, 496, 1, 4, …
## $ statuses_count <int> 18697, 9513, 47674, 634, 634, 288, 221462, 484…
## $ favourites_count <int> 24243, 2857, 25, 253, 253, 485, 5, 680, 5, 897…
## $ account_created_at <dttm> 2015-12-16 11:44:31, 2013-08-27 15:10:28, 202…
## $ verified <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ profile_url <chr> "https://t.co/aBqiu928MF", "http://t.co/EDX7Gt…
## $ profile_expanded_url <chr> "https://disqus.com/by/princezzofthaking/", "h…
## $ account_lang <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ profile_banner_url <chr> "https://pbs.twimg.com/profile_banners/4574806…
## $ profile_background_url <chr> NA, "http://abs.twimg.com/images/themes/theme1…
## $ profile_image_url <chr> "http://pbs.twimg.com/profile_images/947997671…Since columns containing lists can not be stored in flat files like .csv, we’ll need to “flatten” our data in these columns using the aptly named flatten() function from the {rtweets} package:
ccss_tweets_4 <- rtweet::flatten(ccss_tweets_3)Note that we included the package name followed by a double colon rtweet:: when calling our function to avoid confusion with other functions using the same name.
Now we can use the read_csv() function to save our data frame to our folder.
write_csv(ccss_tweets_4, "data/ccss-tweets.csv")Check your data folder to make sure this file has been save. You will also see a ccss-tweets-5.csv file which I have save earlier and which we will use in the next section to ensure we all have the same results.
👉 Your Turn ⤵
Use the search_tweets function to create your own custom query for a twitter hashtag or topic(s) of interest and save as a .csv file in the data folder.
#YOUR CODE HERENote: You may want to use this data for your independent analysis next week.
Other Useful Queries
For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!
For example, let’s create another list containing the usernames of me and some of my colleagues at the Friday Institute using the c() function again and use the get_timelines() function to get the most recent tweets from each of those users:
fi <- c("sbkellogg", "toosweetgeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier")
fi_tweets <- fi %>%
get_timelines(include_rts=FALSE)And let’s use the sample_n() function from the dplyr package to pick 10 random tweets and use select() to select and view just the screenname and text columns that contains the user and the content of their post:
sample_n(fi_tweets, 10) %>%
select(screen_name, text)## # A tibble: 10 x 2
## screen_name text
## <chr> <chr>
## 1 tarheel93 "Tough times call for hard decisions! https://t.co/hL6LZArRm…
## 2 TooSweetGeek "@kareem_carr @threadreaderapp unroll please"
## 3 sbkellogg "Fun with Twitter data coming soon… https://t.co/xKByW65gTu"
## 4 drcallie_tweets "@andrealdesantis Congratulations!!🥳"
## 5 haspires "Congratulations @DrJackson06 and @DrTawan on a new journey!…
## 6 AlexDreier "@tomwilliams777 @SCESROARS Love it, Tom!"
## 7 AlexDreier "For the past 24 hours I have steadfastly refused to look at…
## 8 TooSweetGeek "https://t.co/jaA0EyTLKt https://t.co/5o29DMxbWM"
## 9 drcallie_tweets "Love this thread! 👏🏾👏🏾👏🏾 Congratulations! 🥳 #FailForward ht…
## 10 haspires "@DrJackson06 @DrTawannahAllen Congratulations @DrTawannahAl…We’ve only scratched the surface of the number of functions available in the rtweets package for searching Twitter. Use the following function to take a look at other ways you can query data using the Twitter API:
vignette("intro", package="rtweet")👉 Your Turn ⤵
To conclude Section 2a, try one of the following search functions from the rtweet vignette:
get_timelines()Get the most recent 3,200 tweets from users.stream_tweets()Randomly sample (approximately 1%) from the live stream of all tweets.get_friends()Retrieve a list of all the accounts a user follows.get_followers()Retrieve a list of the accounts following a user.get_favorites()Get the most recently favorited statuses by a user.get_trends()Discover what’s currently trending in a city.search_users()Search for 1,000 users with the specific hashtag in their profile bios.
#YOUR CODE HERE2b. Create Edgelist from Tweets
Recall from Unit 1 that an edge-list that contains information about each tie, or relation between two actors in a network. In our previous walkthrough, a “tie” is a reply by one participant in the discussion forum to the post of another participant – or in some cases to their own post. For our analysis of tweets, we’ll use the same approach used by Supovitz et al. (2017). Specifically, each node is an individual Twitter user (person, group, institution, etc.) and the connection between each node is the tweet, retweet, or mention/reply.
Import Saved Tweets
Before we proceed, let’s first use the read_csv() function from the {readr} package introduced in the Getting Started walkthrough to read the ccss-tweets-5.csv file saved in or data folder so we are all working with the same data:
ccss_tweets_5 <- read_csv("data/ccss-tweets-5.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character(),
## user_id = col_double(),
## status_id = col_double(),
## created_at = col_datetime(format = ""),
## display_text_width = col_double(),
## reply_to_status_id = col_double(),
## reply_to_user_id = col_double(),
## is_quote = col_logical(),
## is_retweet = col_logical(),
## favorite_count = col_double(),
## retweet_count = col_double(),
## quote_count = col_logical(),
## reply_count = col_logical(),
## ext_media_type = col_logical(),
## quoted_status_id = col_double(),
## quoted_created_at = col_datetime(format = ""),
## quoted_favorite_count = col_double(),
## quoted_retweet_count = col_double(),
## quoted_user_id = col_double(),
## quoted_followers_count = col_double(),
## quoted_friends_count = col_double()
## # ... with 26 more columns
## )
## ℹ Use `spec()` for the full column specifications.👉 Your Turn ⤵
As highlighted in Estrellado et al. (2020), cleaning and tidying network data can be even more challenging than for other data sources, network data often includes variables about both individuals and their relationships. This is especially true of our data downloaded using the Twitter API.
Take a look at the data we just imported using one of your favorite methods for inspecting data and in the space below, identify the columns you think could be used to construct an edge list.
- YOUR RESPONSE HERE
Extract Edges and Nodes
If one of the columns you indicated in your response above included screen_name nice work! You may also have noticed that the mentions_screen_name column also includes the names of those in the reply column.
Create Edgelist
Recall from Unit 1 that the first two columns in an edglist should include the nodes that make up a tie or dyad. Since the only two columns we need to construct our edgelist is the screen_name of the tweet author and the screen names of those included in the mentions, let’s relocate() and rename those columns and then select() those columns along with any attributes that we think might be useful for analysis later on, like the timestamp or content of the tweet, i.e. text.
ties_1 <- ccss_tweets_5 %>%
relocate(sender = screen_name, # rename scree_name to sender
target = mentions_screen_name) %>% # rename to receiver
select(sender,
target,
created_at,
text)Our edgelist with attributes is almost ready, but we have a couple issues we still need to deal with.
As you may have noticed, our receiver column contains the names of multiple Twitter users, but a dyad or tie can only be between two actors or nodes.
“Unnest” User Names
Those of you that were in the text mining course
ties_2 <- ties_1 %>%
unnest_tokens(input = target,
output = receiver,
to_lower = FALSE) %>%
relocate(sender, receiver)There is A LOT to unpack with this function. First notice that unnest_tokens expects a data frame as the first argument, which we supplied using teh %>% operator, followed by two column names. The first is an input column that the text comes from, target in this case, which we want to “unnest” into separate rows. Also notice:
Other columns, such as
senderandtext, are retained.By default, words are changed to lowercase, which makes them easier to compare or combine with other datasets. However, we can set the
to_lower = FALSEargument to turn off this behavior since capitalization is important for our user names.
Finally, we’ll introduce the drop_na() function to remove the rows with missing values from our receiver column since they are incomplete dyads. We’ll include these actors in our network later as part of our nodelist in just a bit.
Let’s drop our missing values and save as ties for our final data frame
ties <- ties_2 %>%
drop_na(receiver)👉 Your Turn ⤵
Take a quick look at our final edgelist and answer the following question:
How many edges our in our CCSS network?
- YOUR RESPONSE HERE
Using the tweets you downloaded with your own custom query from above, create an edgelist with any desired edge attributes.
# YOUR CODE HERENode Attributes
The second file we need to create is a data frame that contains all the nodes or actors in our network.
Regardless, let’s read in our node attribute file and take a look at the actors and their attributes included in our dataset:
actors_1 <- ties_2 %>%
pivot_longer(cols = sender:receiver,
names_to = "nodes",
values_to = "screen_name")Since
actors <- actors_1 %>%
select(screen_name) %>%
distinct() %>%
drop_na()👉 Your Turn ⤵
Take a quick look at our final actors data frame and answer the following question:
How many nodes our in our CCSS network?
- YOUR RESPONSE HERE
Using the tweets you downloaded with your own custom query from above, create an nodelist containing all the unique actors in your network.
# YOUR CODE HERE2c. Create Network Object
In Unit 1 we learned about the {igraph} package for preparing network objects for analysis. In this section we introduce a new package that builds upon the “the well-oiled machinery of igraph” but allows us to use some of the familiar functions and syntax from other {tidyverse} packages. In this walkthrough we won’t recreate all the processes like simplifying graphs and adding edge weights, but will demonstrate some features similar and in addition to those found in the {igraph} package.
tidygraph 📦
The {tidygraph} package is a huge package that exports 280 different functions and methods. It more or less wraps the full functionality of igraph in a tidy API giving you access to almost all of the dplyr verbs plus a few more, developed for use with relational data. While network data itself is not tidy, it can be envisioned as two tidy tables, one for node data and one for edge data. The {tidygraph} package provides a way to switch between the two tables and dplyr verbs to manipulate them. Furthermore it provides access to a lot of graph algorithms with return values that facilitate their use in a tidy workflow.
Let’s go ahead and load the {tidygraph} library:
library(tidygraph)##
## Attaching package: 'tidygraph'## The following object is masked from 'package:igraph':
##
## groups## The following object is masked from 'package:stats':
##
## filterCombine Edges & Nodes
Before we can begin using many of the functions from the {tidygraph} package for preparing and summarizing our Twitter network, we first need to convert the data frames that we imported into a network object, similar to what we did in Unit 1.
To do that, we will use the tbl_graph() function and include the following arguments:
edges =expects a data frame, in our caseties, containing information about the edges in the graph. The nodes of each edge must either be in atoandfromcolumn, or in the two first columns like the data frame we provided.nodes =expects a data frame, in our caseactors, containing information about the nodes in the graph. Iftoand/orfromare characters or names, like in our data frames, then they will be matched to the column named according tonode_keyin nodes, if it exists, or matched to the first column in the node list.directed =specifies whether the constructed graph be directed and defaults toTRUEso we did not included it since our network is directed.
Let’s go ahead and create our network graph and name it network
network <- tbl_graph(edges = ties,
nodes = actors)
network## # A tbl_graph: 60 nodes and 49 edges
## #
## # A directed multigraph with 24 components
## #
## # Node Data: 60 x 1 (active)
## screen_name
## <chr>
## 1 DistanceLrnBot
## 2 k12movieguides
## 3 WEquilSchool
## 4 JoeWEquil
## 5 SumayLu
## 6 fluttbot
## # … with 54 more rows
## #
## # Edge Data: 49 x 4
## from to created_at text
## <int> <int> <dttm> <chr>
## 1 1 2 2021-06-28 09:53:54 "#Luca Movie Guide | Worksheet | Questions | …
## 2 3 4 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## 3 3 3 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## # … with 46 more rows👉 Your Turn ⤵
Take a look at the output for our simple graph now and answer the following questions:
Are the numbers and names of nodes and actors consistent with our
actorsandtiesdata frames? What about the integers included in thefromandtocolumns of the Edge Data?- YOUR RESPONSE HERE
What do you think “components” refers to? Hint: see Chapter 6 of (Carolan 2014).
- YOUR RESPONSE HERE
🧶 Knit & Check ✅
Congrats! You made it to the end of data wrangling section and are ready to start analysis! Before proceeding further, knit your document and check to see if you encounter any errors.
3. EXPLORE
As noted in the Getting Started Walkthrough and experienced in Unit 1, exploratory data analysis involves the processes of describing your data (such as by calculating the means and standard deviations of numeric variables, or counting the frequency of categorical variables) and, often, visualizing your data prior to modeling.
In Section 3, we use the {tidygraph} package for retrieving network descriptives and introduce the {ggraph} package to create a network visualization to help illustrate these metrics. Specifically, in this section we’ll learn to:
Examine Basic Descriptives. We focus primarily on actors and edges in this walkthrough, including the edges wights we added in the previous section as well as node degree, and import and fairly intuitive measure of centrality.
Make a Sociogram. Finally, we wrap up the explore phases by learning to plot a network and tweak key elements like the size, shape, and position of nodes and edges to better at communicating key findings.
3a. Examine Basic Descriptives
As we noted in Unit 1, many analyses of social networks are primarily descriptive and aim to either represent the network’s underlying social structure through data-reduction techniques or to characterize network properties through network measures.
Centrality
Node Degree
Recall from Unit 1 that:
Degree is the number of ties to and from an ego. In a directed network, in-degree is the number of ties received, whereas out-degree is the number of ties sent.
The {tidygraph} package has an unique function called activate() that allows us to treat the nodes in our network object as if they were a standard data frame that we can then apply standard tidyverse functions to like select(), filter(), and mutate().
The latter function, mutate(), we can use to create new variables for nodes such as measures of degree, in-degree, and out-degree using the centrality_degree() function in the {tidygraph} package.
Run the following code to add degree measures to each of our nodes and print the output:
network_1 <- network %>%
activate(nodes) %>%
mutate(degree = centrality_degree(mode = "all")) %>%
mutate(in_degree = centrality_degree(mode = "in")) %>%
mutate(out_degree = centrality_degree(mode = "out"))
network_1## # A tbl_graph: 60 nodes and 49 edges
## #
## # A directed multigraph with 24 components
## #
## # Node Data: 60 x 4 (active)
## screen_name degree in_degree out_degree
## <chr> <dbl> <dbl> <dbl>
## 1 DistanceLrnBot 1 0 1
## 2 k12movieguides 1 1 0
## 3 WEquilSchool 6 3 3
## 4 JoeWEquil 4 2 2
## 5 SumayLu 11 3 8
## 6 fluttbot 3 0 3
## # … with 54 more rows
## #
## # Edge Data: 49 x 4
## from to created_at text
## <int> <int> <dttm> <chr>
## 1 1 2 2021-06-28 09:53:54 "#Luca Movie Guide | Worksheet | Questions | …
## 2 3 4 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## 3 3 3 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## # … with 46 more rowsWe now see that these simple measures of centrality have been added to the nodes in our network.
We can also use the activate() function combined with the data.frame() function to extract our new measures to a separate data frame so we inspect our nodes individually and create some summary statistics using the handy summary() function.
node_measures <- network_1 %>%
activate(nodes) %>%
data.frame()
summary(node_measures)## screen_name degree in_degree out_degree
## Length:60 Min. : 0.000 Min. :0.0000 Min. :0.0000
## Class :character 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:0.0000
## Mode :character Median : 1.000 Median :1.0000 Median :0.0000
## Mean : 1.633 Mean :0.8167 Mean :0.8167
## 3rd Qu.: 2.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :11.000 Max. :5.0000 Max. :8.0000Despite a dramatic size difference from our network in Unit 1, we see that typical nodes in this network also have relatively few connections, though there are a few exceptions.
👉 Your Turn ⤵
Recall from the Prepare section that one of our questions guiding this analysis was:
Who are the transmitters, transceivers, and transcenders in our Common Core Twitter network?
Use the code chunk below to inspect our node_measures data frame and answer the questions above in the space below:
#YOUR CODE HERE- YOUR RESPONSE HERE
Other Centrality Measures
In Chapter 7 of Carolan (2014), noted that degree centrality does not take into account indirect ties among all the alters in an ego’s network. We were also introduced to a few other measures of centrality commonly used in network analysis and applied to educational contexts:
Closeness includes data about the relation between each pair of ego’s named alters and is intuitively appealing in that being “close” to others and may indicate how quickly an actor can exchange something with others or be the first to receive information.
Betweenness captures how actors control or mediate the relations between pairs of actors that are not directly connected and is an important indicator of control over information exchange or resource flows within a network.
Carolyn also notes that in addition to these three common centrality measures, at many others have been developed and can be calculated in most common social network-analysis software applications. In fact, {tidygraph} includes many of these measures and includes various centrality_ functions for calculating node and edge centrality. Type ?centrality in your console below and hit enter to view them all.
👉 Your Turn ⤵
Use the code chunk below to add these closeness and betweenness measures to our network_1 data frame and save as network_2. I’ve included some basic code to get your started.
network_2 <- network_1
#YOUR CODE HERE3b. Make a Sociogram
If you recall from Unit 1, network visualization can be used for a variety of purposes, ranging from highlighting key actors to even serving as works of art. These visual representations of the actors and their relations, i.e. the network, are called a sociogram. Actors who are most central to the network, such as those with higher node degrees, are usually placed in the center of the sociogram and their ties are placed near them. In this section, we’ll briefly introduce the {ggraph} package for creating attractive network
ggraph 📦
Created by the same developer as {tidygraph}, {ggraph} – pronounced gg-raph or g-giraffe hence the logo – is an extension of {ggplot} aimed at supporting relational data structures such as networks, graphs, and trees. Both packages are more modern and widely adopted approaches data visualization in R.
While ggraph builds upon the foundation of ggplot and its API, it comes with its own self-contained set of geoms, facets, etc., as well as adding the concept of layouts to the grammar of graphics, i.e. the “gg” in ggplot and ggraph.
Let’s go ahead and load the {ggraph} library:
library(ggraph)A Simple Sociogram
Very similar to how functions in the tidyverse use the %>% operator to “pipe” functions together to progressively wrangle data, ggraph and ggrplot use the + operator to “layer” functions together to progressively build graphs.
Let’s start with the first and simplest function ggraph and supply our network graph:
ggraph(network_1)## Using `stress` as default layout
As you can see, this didn’t produce much. All this function does is take care of setting up the network object to plot along with creating the layout for the plot based on the graph and the specified layout passed in.
Add Layout
Let’s go ahead and include the layout argument, which in addition to including its own unique layouts, can incorporate layouts form {igraph} like fr.
ggraph(network_1, layout = "fr")
Add Nodes
Still nothing but that is because we haven’t added the nodes yet. Let’s do that:
ggraph(network_1, layout = "fr") +
geom_node_point() 
Well, at least we have our nodes now!
The “geom” in the geom_non_point() functions stands for “Geometric elements,” or geoms for short, and represent what you actually see in the plot.
These geoms can include aesthetics, or aes for short, such as alpha for transparency, as well as colour, shape and size.
Let’s now add some “aesthetics” to our points by including the aes() function and arguments such as size = which we can set to our in_degree measures:
ggraph(network_1, layout = "fr") +
geom_node_point(aes(size = in_degree,
alpha = out_degree,
colour = degree))
And let’s add some node text and labels while were at it since this is not a very large network. Since node labels are a geometric element, we can apply aesthetics to them as well. Let’s also include the repel = argument that when set to TRUE will avoid overlapping text.
ggraph(network_1, layout = "fr") +
geom_node_point(aes(size = in_degree,
alpha = out_degree,
colour = degree)) +
geom_node_text(aes(label = screen_name,
size = in_degree/2,
alpha = degree),
repel=TRUE)
Much better! Even without the edges, using size and opacity has helped to illustrate some of the “transmitters,” “trancievers” and “transcenders.”
Add Edges
Now, let’s connect the dots and add some edges that include some arrows 1mm in length as well as an end cap to keep them from overlapping the nodes:
ggraph(network_1, layout = "fr") +
geom_node_point(aes(size = in_degree,
alpha = out_degree,
colour = degree)) +
geom_node_text(aes(label = screen_name,
size = degree/2,
alpha = degree),
repel=TRUE) +
geom_edge_link(arrow = arrow(length = unit(1, 'mm')),
end_cap = circle(3, 'mm'),
alpha = .3)
Add a Theme
Finally, let’s add a theme, which controls the finer points of display, like the font size and background color. The theme_graph() function add a theme specially tuned for graph visualizations. This function removes redundant elements in order to put focus on the data and if you type ?theme_graph in the console you will get a sense of the level of fine tuning you can do if desired.
Let’s add theme_graph() to our sociogram and call it good for now:
ggraph(network_1, layout = "fr") +
geom_node_point(aes(size = in_degree,
alpha = out_degree,
colour = degree)) +
geom_node_text(aes(label = screen_name,
size = degree/2,
alpha = degree),
repel=TRUE) +
geom_edge_link(arrow = arrow(length = unit(1, 'mm')),
end_cap = circle(3, 'mm'),
alpha = .3) +
theme_graph()
👉 Your Turn ⤵
Try modifying the code below by tweaking the included function/arguments or adding new ones for layouts, nodes, and edges to make our plot either more “aesthetically pleasing” or more purposeful in what it’s trying to communicate.
There are no right or wrong answers, just have some fun trying out different approaches!
ggraph(network_1, layout = "kk") +
geom_node_point(aes(size = in_degree,
alpha = out_degree,
colour = degree)) +
geom_node_text(aes(label = screen_name,
size = degree/2,
alpha = degree),
repel=TRUE) +
geom_edge_link(arrow = arrow(length = unit(1, 'mm')),
end_cap = circle(3, 'mm'),
alpha = .3) +
theme_graph()
🧶 Knit & Check ✅
Congrats! You made it to the end of the Explore section and are ready to learn a little about network modeling! Before proceeding further, knit your document and check to see if you encounter any errors.
4. MODEL
As highlighted in Chapter 3 of Data Science in Education Using R, the Model step of the data science process entails “using statistical models, from simple to complex, to understand trends and patterns in the data.”
4a. Identify Groups
In Chapter 6: Groups and Positions in Complete Networks of SNA and Education (Carolan 2014) we were introduced to both “bottom up” and “top down” approaches for identifying groups in a network, as well as why researchers may be interested in exploring these groups. He also notes that:
Unlike most social science, the idea is to identify these groups through their relational data, not an exogenous attribute such as grade level, departmental affiliation, or years of experience.
In this section, we’ll briefly explore a “top down” approach to identifying these groups through the use of community detection algorithms.
Community Detection
Similar to the range of functions included for calculating node and edge centrality, the {tidygraph} package includes various clustering functions provided by the {igraph} package introduced in Unit 1.
Also similar to calculating centrality measures, we need to activate() our nodes first before applying these community detection algorithms to assign our nodes to groups.
Run the following code and take a print our new network graph to the console to take a quick look
network_3 <- network_2 %>%
activate(nodes) %>%
mutate(group = group_infomap())
network_3## # A tbl_graph: 60 nodes and 49 edges
## #
## # A directed multigraph with 24 components
## #
## # Node Data: 60 x 5 (active)
## screen_name degree in_degree out_degree group
## <chr> <dbl> <dbl> <dbl> <int>
## 1 DistanceLrnBot 1 0 1 12
## 2 k12movieguides 1 1 0 12
## 3 WEquilSchool 6 3 3 1
## 4 JoeWEquil 4 2 2 1
## 5 SumayLu 11 3 8 1
## 6 fluttbot 3 0 3 1
## # … with 54 more rows
## #
## # Edge Data: 49 x 4
## from to created_at text
## <int> <int> <dttm> <chr>
## 1 1 2 2021-06-28 09:53:54 "#Luca Movie Guide | Worksheet | Questions | …
## 2 3 4 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## 3 3 3 2021-06-28 02:32:59 "Why public schools should focus more on buil…
## # … with 46 more rowsNote: Some of these algorithms are designed for directed graphs, while others for undirected graphs.
Now that we’ve assigned our nodes to a group, let’s modify our sociogram from above to color our nodes by group assignment and remove the alpha = argument to make them a little easier to see.
network_3 %>%
ggraph(layout = "kk") +
geom_node_point(aes(size = in_degree,
colour = group)) +
geom_node_text(aes(label = screen_name,
size = degree/2,
alpha = degree),
repel=TRUE) +
geom_edge_link(arrow = arrow(length = unit(1, 'mm')),
end_cap = circle(3, 'mm'),
alpha = .3) +
theme_graph()
👉 Your Turn ⤵
Recall from the Prepare section that the second question guiding this analysis was:
What subgroups, or factions, exist in our network?
Use the code chunk below to extract the group assignment data frame and answer the question above in the space below:
#YOUR CODE HERE- YOUR RESPONSE HERE
4b. Identify Sentiment
Sentiment analysis (also known as opinion mining) is a text mining technique used to “systematically identify, extract, quantify, and study affective states and subjective information.”
In this section, we’ll introduce and apply the {vader} package to gain some
The vader Package 📦
The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.
To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text.
Let’s go ahead and load the vader library:
library(vader)The {vader} package basically has just one function, vader_df() that does one thing and expects just one column from a data frame. Let’s give VADER the our ccss_tweets_6 data frame that we created earlier and include the $ operator to select for analysis our text column containing our tweets.
summary_vader <- vader_df(ties$text)
summary_vader## text
## 1 #Luca Movie Guide | Worksheet | Questions | Google Classroom (PG - 2021) challenges students to analyze characters, their decisions and relationships. #SeaMonster #GoogleClassroom #GoogleForms #Teachers #MovieGuides #CCSS #Homeschooling #DistanceLearning\n\nhttps://t.co/ENIuEHNUB0
## 2 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 3 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 4 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 5 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 6 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 7 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 8 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 9 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 10 Here's how you make a generation of mentally crippled, government dependant sheep. #commoncore @math #educational #funny #conedy https://t.co/ttdiAxEuut
## 11 @sfchronicle This is even more evidence that we need a complete overhaul of the American educational system to plan for the wildly different tech connected job force of now and the next 50 years\n\n#OBE, #CRT, #CommonCore and dated curricula are failing our children
## 12 If we’re going to talk about how to teach students to write stories, we should start by thinking about why we tell stories at all.\n#engchat #teachingstrategies #CCSS #edchat #teachwriting #teaching\nhttps://t.co/7OZSEz7CuH
## 13 Stories of Us: Kari Lake\nThis is how #FakeNews has taken root. It starts in K-12 and the indoctrination continues in College. \n\nWe can't get America back on track, if we don't START by fixing EDUCATION!\nhttps://t.co/0TDvTa0jqi via @YouTube \n#StopCommonCore #StopHR1814 #StopS879
## 14 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 15 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 16 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 17 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 18 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 19 @AuldShaneGamer Why? What’s wrong with #CommonCore?
## 20 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 21 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 22 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 23 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 24 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 25 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 26 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 27 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 28 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 29 Climate change: Large-scale CO2 removal facility set for Scotland\nhttps://t.co/pAzFQ8RPi4\n#ClimateChamge #ClimateCrisis #ClimateAction #CCSS
## 30 Math + tech + creativity = Engaged learners! Free guide with ideas and lesson plans https://t.co/DhAjX94YCt #ccss https://t.co/7rUKchI27U
## 31 Summer is officially here! https://t.co/JPqkYd9Gzk Kick yours off by following this 2nd grader's instructions for making lemonade #howto #ccss https://t.co/amCmiXjHHx
## 32 WriteReader has aligned all writing prompts and book templates to the Elementary Texas Essential Knowledge and Skills for English Language Arts and Reading and the Common Core State Standards for Writing K-5. Read more below #literacy #commoncore\nhttps://t.co/OSpwojGhXU https://t.co/9ev3VepQIk
## 33 RT @DoD_DHA : Don't miss your opportunity to sign up for the #DHA Continuing Education Program Office's, Clinical Communities Speaker Series (#CCSS), an evidence-based medicine training event happening June 24th from 7:45 a.m. – 4:15 p.m. \n\nTo learn more… https://t.co/of56oLAQG9
## 34 @MarshaBlackburn Between that and #CommonCore they’re really hell bent on dumbing down our children and making them all non-critical thinking sheep.
## 35 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 36 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 37 In the wake of Common Core, English teacher Joy Pullmann uncovers the consequences of these curriculum standards in her book The Education Invasion.\n#BookRecommendations #commoncore\n https://t.co/ztB1TTAe8x https://t.co/P1kcfWj9hR
## 38 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 39 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 40 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 41 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 42 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 43 @RealSaltySlim Must be that new #CommonCore science graduate.
## 44 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 45 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 46 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 47 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 48 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 49 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## word_scores
## 1 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 2 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 3 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 4 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 5 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 6 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 7 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 8 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 9 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 10 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 11 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0}
## 12 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 13 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 14 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 15 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 16 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 17 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 18 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 19 {0, 0, 0, -2.1, 0, 0}
## 20 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 21 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 22 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 23 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 24 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 25 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 26 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 27 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 28 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 29 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 30 {0, 0, 0, 0, 1.6, 0, 1.7, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 31 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 32 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 33 {0, 0, 0, 0, 0.444, 0, -1.332, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 34 {0, 0, 0, 0, 0, 0, 0, -3.893, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 35 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 36 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 37 {0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 38 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 39 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 40 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 41 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 42 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 43 {0, 0, 0, 0, 0, 0, 0, 0}
## 44 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 45 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 46 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 47 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 48 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 49 {0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## compound pos neu neg but_count
## 1 0.077 0.042 0.958 0.000 0
## 2 0.144 0.113 0.792 0.095 0
## 3 0.144 0.113 0.792 0.095 0
## 4 0.144 0.113 0.792 0.095 0
## 5 0.144 0.113 0.792 0.095 0
## 6 0.144 0.113 0.792 0.095 0
## 7 0.144 0.113 0.792 0.095 0
## 8 0.144 0.113 0.792 0.095 0
## 9 0.144 0.113 0.792 0.095 0
## 10 0.440 0.146 0.854 0.000 0
## 11 -0.511 0.000 0.929 0.071 0
## 12 0.000 0.000 1.000 0.000 0
## 13 0.000 0.000 1.000 0.000 0
## 14 0.494 0.211 0.789 0.000 0
## 15 0.494 0.211 0.789 0.000 0
## 16 0.402 0.184 0.816 0.000 0
## 17 0.402 0.184 0.816 0.000 0
## 18 0.402 0.184 0.816 0.000 0
## 19 -0.536 0.000 0.591 0.409 0
## 20 0.000 0.000 1.000 0.000 0
## 21 0.000 0.000 1.000 0.000 0
## 22 0.000 0.000 1.000 0.000 0
## 23 0.000 0.000 1.000 0.000 0
## 24 -0.566 0.000 0.870 0.130 1
## 25 -0.566 0.000 0.870 0.130 1
## 26 -0.566 0.000 0.870 0.130 1
## 27 -0.566 0.000 0.870 0.130 1
## 28 -0.566 0.000 0.870 0.130 1
## 29 0.000 0.000 1.000 0.000 0
## 30 0.836 0.372 0.628 0.000 0
## 31 0.000 0.000 1.000 0.000 0
## 32 0.000 0.000 1.000 0.000 0
## 33 -0.223 0.035 0.910 0.056 0
## 34 -0.750 0.000 0.748 0.252 0
## 35 0.000 0.000 1.000 0.000 0
## 36 0.000 0.000 1.000 0.000 0
## 37 0.586 0.128 0.872 0.000 0
## 38 0.000 0.000 1.000 0.000 0
## 39 -0.161 0.053 0.879 0.068 0
## 40 -0.161 0.053 0.879 0.068 0
## 41 -0.161 0.053 0.879 0.068 0
## 42 -0.161 0.053 0.879 0.068 0
## 43 0.000 0.000 1.000 0.000 0
## 44 -0.161 0.053 0.879 0.068 0
## 45 -0.161 0.053 0.879 0.068 0
## 46 -0.161 0.053 0.879 0.068 0
## 47 -0.161 0.053 0.879 0.068 0
## 48 -0.161 0.053 0.879 0.068 0
## 49 -0.161 0.053 0.879 0.068 0Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their GitHub repository and I’ve copied and explanation of the scores below:
- The
compoundscore is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a ‘normalized, weighted composite score’ is accurate.
NOTE: The compound score is the one most commonly used for sentiment analysis by most researchers, including the authors.
Let’s use the inner_join() function to add these sentiment scores values back to our ties data frame and take a quick look:
tweet_sentiment <-inner_join(summary_vader,
ties,
by = "text")
tweet_sentiment## text
## 1 #Luca Movie Guide | Worksheet | Questions | Google Classroom (PG - 2021) challenges students to analyze characters, their decisions and relationships. #SeaMonster #GoogleClassroom #GoogleForms #Teachers #MovieGuides #CCSS #Homeschooling #DistanceLearning\n\nhttps://t.co/ENIuEHNUB0
## 2 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 3 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 4 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 5 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 6 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 7 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 8 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 9 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 10 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 11 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 12 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 13 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 14 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 15 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 16 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 17 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 18 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 19 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 20 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 21 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 22 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 23 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 24 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 25 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 26 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 27 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 28 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 29 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 30 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 31 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 32 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 33 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 34 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 35 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 36 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 37 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 38 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 39 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 40 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 41 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 42 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 43 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 44 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 45 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 46 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 47 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 48 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 49 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 50 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 51 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 52 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 53 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 54 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 55 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 56 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 57 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 58 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 59 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 60 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 61 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 62 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 63 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 64 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 65 Why public schools should focus more on building #Confidence instead of forced #Learning.\n\n@WEquilSchool @SumayLu #Flutterista #Flutter #CommonCore #EdTech #EducationRevolution #Homeschooling #CharterSchool #Unschooling $LRN $COUR \n\nhttps://t.co/ZHGCflphcP https://t.co/dkKgLaHKm0
## 66 Here's how you make a generation of mentally crippled, government dependant sheep. #commoncore @math #educational #funny #conedy https://t.co/ttdiAxEuut
## 67 @sfchronicle This is even more evidence that we need a complete overhaul of the American educational system to plan for the wildly different tech connected job force of now and the next 50 years\n\n#OBE, #CRT, #CommonCore and dated curricula are failing our children
## 68 If we’re going to talk about how to teach students to write stories, we should start by thinking about why we tell stories at all.\n#engchat #teachingstrategies #CCSS #edchat #teachwriting #teaching\nhttps://t.co/7OZSEz7CuH
## 69 Stories of Us: Kari Lake\nThis is how #FakeNews has taken root. It starts in K-12 and the indoctrination continues in College. \n\nWe can't get America back on track, if we don't START by fixing EDUCATION!\nhttps://t.co/0TDvTa0jqi via @YouTube \n#StopCommonCore #StopHR1814 #StopS879
## 70 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 71 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 72 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 73 @catturd2 @JohnPetersonFW Makes total sense when one incorporates “My Friend Ten”… #commoncore \U0001f974
## 74 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 75 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 76 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 77 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 78 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 79 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 80 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 81 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 82 @ConsiderThis19 @hapybrian @stillgray What needs to change about #CommonCore to help teach #CriticalThinking?
## 83 @AuldShaneGamer Why? What’s wrong with #CommonCore?
## 84 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 85 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 86 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 87 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 88 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 89 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 90 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 91 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 92 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 93 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 94 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 95 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 96 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 97 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 98 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 99 @kalidasstw @FriedrichFiles @WSBRetard2 @nucholibre What need to change about #CommonCore?
## 100 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 101 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 102 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 103 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 104 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 105 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 106 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 107 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 108 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 109 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 110 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 111 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 112 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 113 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 114 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 115 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 116 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 117 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 118 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 119 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 120 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 121 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 122 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 123 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 124 Abstract from #ASCO21 - work from @DrRebeccaHowel1 @LouisSConstine @SShrestha_1 @HoppeBrad and many more on cardiac substructure dosimetry in 25,481 survivors of childhood cancer in the #CCSS\n\n- Mean heart dose <10 Gy not associated with increased risk of cardiac diseases\n\nBUT...
## 125 Climate change: Large-scale CO2 removal facility set for Scotland\nhttps://t.co/pAzFQ8RPi4\n#ClimateChamge #ClimateCrisis #ClimateAction #CCSS
## 126 Math + tech + creativity = Engaged learners! Free guide with ideas and lesson plans https://t.co/DhAjX94YCt #ccss https://t.co/7rUKchI27U
## 127 Summer is officially here! https://t.co/JPqkYd9Gzk Kick yours off by following this 2nd grader's instructions for making lemonade #howto #ccss https://t.co/amCmiXjHHx
## 128 WriteReader has aligned all writing prompts and book templates to the Elementary Texas Essential Knowledge and Skills for English Language Arts and Reading and the Common Core State Standards for Writing K-5. Read more below #literacy #commoncore\nhttps://t.co/OSpwojGhXU https://t.co/9ev3VepQIk
## 129 RT @DoD_DHA : Don't miss your opportunity to sign up for the #DHA Continuing Education Program Office's, Clinical Communities Speaker Series (#CCSS), an evidence-based medicine training event happening June 24th from 7:45 a.m. – 4:15 p.m. \n\nTo learn more… https://t.co/of56oLAQG9
## 130 @MarshaBlackburn Between that and #CommonCore they’re really hell bent on dumbing down our children and making them all non-critical thinking sheep.
## 131 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 132 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 133 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 134 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 135 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 136 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 137 In the wake of Common Core, English teacher Joy Pullmann uncovers the consequences of these curriculum standards in her book The Education Invasion.\n#BookRecommendations #commoncore\n https://t.co/ztB1TTAe8x https://t.co/P1kcfWj9hR
## 138 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 139 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 140 Onto text mining - collecting, processing, and analyzing text-based data! Comparing the #NGSS and #CCSS @LASER_Institute #LASERInstitute https://t.co/kwxd8LWBLn
## 141 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 142 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 143 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 144 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 145 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 146 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 147 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 148 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 149 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 150 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 151 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 152 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 153 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 154 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 155 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 156 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 157 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 158 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 159 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 160 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 161 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 162 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 163 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 164 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 165 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 166 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 167 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 168 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 169 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 170 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 171 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 172 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 173 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 174 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 175 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 176 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 177 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 178 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 179 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 180 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 181 @RealSaltySlim Must be that new #CommonCore science graduate.
## 182 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 183 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 184 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 185 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 186 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 187 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 188 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 189 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 190 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 191 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 192 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 193 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 194 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 195 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 196 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 197 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 198 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 199 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 200 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 201 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 202 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 203 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 204 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 205 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 206 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 207 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 208 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 209 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 210 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 211 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 212 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 213 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 214 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 215 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 216 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 217 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 218 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 219 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 220 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 221 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 222 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 223 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 224 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 225 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 226 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 227 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 228 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 229 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 230 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 231 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 232 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 233 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 234 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 235 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 236 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 237 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 238 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 239 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 240 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## 241 @arneduncan NO evidence for #commoncore, teacher evaluation based on student test scores, or push to expand online learning. Yet @usedgov under your misleadership pushed them on schools & students nonetheless. Not to mention yr opposition to research-based class size reduction.
## word_scores
## 1 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 2 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 3 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 4 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 5 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 6 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 7 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 8 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 9 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 10 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 11 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 12 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 13 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 14 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 15 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 16 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 17 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 18 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 19 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 20 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 21 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 22 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 23 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 24 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 25 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 26 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 27 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 28 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 29 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 30 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 31 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 32 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 33 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 34 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 35 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 36 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 37 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 38 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 39 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 40 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 41 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 42 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 43 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 44 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 45 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 46 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 47 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 48 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 49 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 50 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 51 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 52 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 53 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 54 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 55 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 56 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 57 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 58 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 59 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 60 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 61 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 62 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 63 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 64 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 65 {0, 0, 0, 0, 0, 0, 0, 0, 2.5637, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 66 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 67 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0}
## 68 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 69 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 70 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 71 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 72 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 73 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 74 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 75 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 76 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 77 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 78 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 79 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 80 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 81 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 82 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 83 {0, 0, 0, -2.1, 0, 0}
## 84 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 85 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 86 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 87 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 88 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 89 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 90 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 91 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 92 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 93 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 94 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 95 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 96 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 97 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 98 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 99 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 100 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 101 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 102 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 103 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 104 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 105 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 106 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 107 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 108 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 109 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 110 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 111 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 112 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 113 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 114 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 115 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 116 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 117 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 118 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 119 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 120 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 121 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 122 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 123 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 124 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.407, -0.55, 0, 0, 0, 0}
## 125 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 126 {0, 0, 0, 0, 1.6, 0, 1.7, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 127 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 129 {0, 0, 0, 0, 0.444, 0, -1.332, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 130 {0, 0, 0, 0, 0, 0, 0, -3.893, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 131 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 132 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 133 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 134 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 241 2021-06-19 16:27:32Overall, the CCSS tweets lean slightly negative as we would expect.
user_sentiment <- tweet_sentiment %>%
group_by(sender) %>%
summarise(sentiment = mean(compound))
user_sentiment## # A tibble: 25 x 2
## sender sentiment
## <chr> <dbl>
## 1 AuldShaneGamer 0.586
## 2 BodShameless 0.44
## 3 carolburris -0.161
## 4 CivicViewPoint 0
## 5 ConsiderThis19 -0.75
## 6 deutsch29blog -0.161
## 7 DistanceLrnBot 0.077
## 8 DouglasHoltMD -0.566
## 9 dougrafnson 0.494
## 10 DrZahwa -0.223
## # … with 15 more rowsNote that we have effectively just created some new node and edge “attributes” that could be incorporated into our network visualization to potentially help understand why groups may have formed as they did.
👉 Your Turn ⤵
Recall from the Prepare section that the final question guiding this analysis was:
Which actors in our network tend to be more opposed to the Common Core?
Use the code chunk below to inspect our data frame and answer the question above in the space below:
#YOUR CODE HERE- YOUR RESPONSE HERE
5. COMMUNICATE
For your Independent Analysis assignment for Unit 2 next week, you’ll create either a simple report or slide deck using an R Markdown document just like this to share out some key findings from your analysis. Regardless of whether you plan to talk us through your analysis and findings with a presentation or walk us through with a brief written report, your presentation or report should address the following questions:
Purpose. What question or questions are guiding your analysis? What did you hope to learn by answering these questions and why should your audience care about your findings?
Methods. What data did you selected for analysis? What steps did you take took to prepare your data for analysis and what techniques you used to analyze your data? These should be fairly explicit with your embedded code.
Findings. What did you ultimately find? How do your “data products” help to illustrate these findings? What conclusions can you draw from your analysis?
Discussion. What were some of the strengths and weaknesses of your analysis? How might your audience use this information? How might you revisit or improve upon this analysis in the future?
👉 Your Turn ⤵
Now that you’ve become more familiar with this dataset and the social network perspective, what other aspects of this dataset, or a dataset you are interested in exploring, could you investigate?
- YOUR RESPONSE HERE
What specific research questions might you ask that would be helpful for being understanding and improving learning, or the context in which the data is collected? How could other approaches like sentiment analysis
- YOUR RESPONSE HERE
🧶 Knit & Check ✅
Congrats! You’ve finished the Unit 2 Guided Walkthrough and are ready for some independent analysis next week!
To complete this assignment, knit your document and send me an email at sbkellog@ncsu.edu letting me know you’re all set.